Stanford chemistry Vijay Pande and his students see a future for machine learning in the early stages of drug development. (Image credit: L.A. Cicero)
Combining computer science and chemistry, researchers show how an advanced form of machine learning that works off small amounts of data can be used to solve problems in drug discovery. Artificially intelligent algorithms can learn to identify amazingly subtle information, enabling them to distinguish between people in photos or to screen medical images as well as a doctor. But in most cases their ability to perform such feats relies on training that involves thousands to trillions of data points. This means artificial intelligence doesn't work all that well in situations where there is very little data, such as drug development. Vijay Pande , professor of chemistry at Stanford University, and his students thought that a fairly new kind of deep learning, called one-shot learning, that requires only a small number of data points might be a solution to that low-data problem. 'We're trying to use machine learning, especially deep learning, for the early stage of drug design,' said Pande. 'The issue is, once you have thousands of examples in drug design, you probably already have a successful drug.
TO READ THIS ARTICLE, CREATE YOUR ACCOUNT
And extend your reading, free of charge and with no commitment.
